A logistic-based method for rice monitoring from multitemporal MODIS-Landsat fusion data

Abstract

Information on rice cropping activities and growing areas is critical for crop management. This study developed a logistic-based method to monitor rice sowing and harvesting activities and, accordingly, to map rice growing areas from the MODIS-Landsat fusion data in An Giang Province, Vietnam. The EVI2 data derived from the fusion data compared with that of Landsat data indicated a close correlation (R2 = 0.93). The comparisons between the estimated sowing and harvesting dates and the field survey data revealed the RMSE values of around 8 and 5 days for the winter-spring crop and 9 and 12 days for the summer-autumn crop, respectively. The rice mapping results compared with the ground reference data indicated an overall accuracy and Kappa coefficient of 93.2% and 0.86 for the winter-spring crop, and 91.7% and 0.83 for the summer-autumn crop, respectively. These results were reaffirmed by the government's rice areas statistics, with the relative error in area values smaller than 3.3%.

Remotely sensed phenological heterogeneity of restored wetlands: linking vegetation structure and function

“…Fitting a curve to per-pixel phenological series of EVI is an important step in the analyses because it allows interpolating greenness (spectral vegetation index) values from discrete observations of available image dates to a daily time step. While a variety of algorithms and functions have been proposed for such interpolations, studies focusing on vegetation with deciduous seasonality have often considered sigmoid logistic functions consistent with the non-linear dynamics of greenness proxies ( Figure S2), i.e., more rapid changes during the early season green-up and late season senescence and low variation during the low greenness (i.e., before the start of greening and after the end of senescence) and high greenness (between end of greening and start of senescence) phases Son et al, 2016;Xu et al, 2014). A double-logistic function captures both increase (green-up) and decrease (senescence) phases by a single equation with different parameter sets for each phase to the Enhanced Vegetation Index (EVI) series:…”

Contribution of Biophysical Factors to Regional Variations of Evapotranspiration and Seasonal Cooling Effects in Paddy Rice in South Korea

“…Boschetti et al [37] compared the LGP and SoS derived from the TIMESAT and field census, respectively, by rice farmers, indicating a good correlation (R 2 = 0.92). Meanwhile, Son et al [38] proved that rice sowing and harvesting dates could be exactly retrieved from MODIS VIs using the double logistic-based method. In line with these studies, the built-in noise reduction techniques in the TIMESAT program were used in this study to process time series of MODIS VIs, i.e., the spike method of median filtering, envelope iterations, minimum of 0.25, 0.1, and 0.05 in NDVI, EVI, and OSAVI, respectively, the fitting method based on the double logistic-based model, and the start of season method (i.e., STL trend).…”

Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions

“…Although the short revisit cycle of these images can successfully monitor rapid dynamics in agriculture, the coarse resolution is often inadequate for highly heterogeneous areas such as agricultural landscapes in south China. In recent years, the high spatial and temporal resolution images generated by spatiotemporal data fusion have been explored to improve agricultural studies, such as, crop yield and gross primary productivity (GPP) estimation, crop growth monitoring and management [82][83][84][85].…”